B-036 Digital Visual Management as a Lean Governance Tool in Clinical Laboratory Operations: Implementation and Early Operational Impact of a Customized Real-Time Dashboard

Abstract

Increasing competitive pressure and operational complexity have intensified the need for robust governance models in clinical laboratory environments. Digital visual management (DVM) has emerged as a strategic approach to enhance transparency, coordination, and decision-making through real-time visualization of operational data. While often deployed as a supportive management layer, DVM has increasingly been recognized as an independent lean practice capable of enabling sustained operational improvement. This study describes the development, implementation, and early operational impact of a customized DVM system in a large-scale clinical laboratory, with emphasis on governance, workflow reliability, and interdisciplinary communication. We adopted a structured, participatory implementation model involving laboratory leadership, information technology specialists, automation technicians, and frontline operational teams. Data capture was performed using Microsoft Forms integrated with a centralized Excel database within the Microsoft 365 ecosystem. Conditional logic routed submissions into two standardized workflows: (1) equipment release and (2) operational incident reporting. For equipment release, analysts selected workstations from a predefined list and recorded operational status, performance indicators, and instrument readiness for routine sample processing using structured fields. For incident reporting, users classified event criticality, identified the affected workstation, and documented root causes, immediate responses, and corrective actions using free-text fields. All records were automatically synchronized to the linked database and made accessible to staff across multiple workstations. Data were consolidated, validated, and transformed into interactive dashboards using Microsoft Power BI. Visualizations were organized into three operational views: equipment availability, immediate disruptions, and recurrent system-level issues. Prior to publication, entries were reviewed by an automation analyst for relevance, accuracy, and internal consistency. Dashboards were continuously displayed on large-format monitors in the technical operations center, enabling uninterrupted, real-time access for all technical departments. After four weeks of iterative alignment meetings and system refinement, a stable data and visualization architecture was established. Engagement from pre-analytical, analytical, and post-analytical teams enabled comprehensive real-time operational visibility. Qualitative assessment indicated that the dashboard supported earlier identification of equipment unavailability, workflow bottlenecks, and recurrent operational vulnerabilities. Team feedback highlighted improved cross-functional communication, faster escalation of critical events, and greater consistency in response strategies. The platform enhanced shared situational awareness, reduced information fragmentation, and improved alignment between operational priorities and coordination-level decision-making. Implementing an integrated digital visual management platform strengthened laboratory governance by improving transparency, accountability, and data-informed decision-making. By transforming decentralized operational records into cohesive, real-time visual workflows, the system enabled proactive management and continuous performance optimization. These findings support DVM as a high-impact lean practice for complex laboratory environments and demonstrate its potential to enhance operational resilience, efficiency, and long-term sustainability.